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ae08e77979
* fix(scheduler): give worker tasks a real per-attempt execution deadline The plugin scheduler derived the per-attempt execution deadline as DetectionTimeoutSeconds * 2, which capped every worker task at twice the cluster-scan budget regardless of actual work. For volume_balance batches this was 240s — far too short for 20 large volume copies, so every attempt died at "context deadline exceeded" and all in-flight sub-RPCs surfaced as "context canceled". Retries restarted from move 1 and hit the same wall. Add an explicit ExecutionTimeoutSeconds field to the plugin proto and make each handler declare its own baseline (1800s for vacuum, balance, EC; 3600s for iceberg). Size-aware handlers also emit an estimated_runtime_seconds parameter on each proposal so the scheduler extends the per-attempt deadline based on actual workload: - volume_balance batch: max(largest single move, total / concurrency) at 5 min/GB, so a skewed batch with one big volume isn't averaged away. - volume_balance single, vacuum (already), erasure_coding (10 min/GB), ec_balance (5 min/GB): per-volume budgets. admin_script and iceberg keep the configurable handler default since their workloads are opaque to the detector. * fix(scheduler): apply descriptor defaults to existing persisted configs The previous commit added execution_timeout_seconds to the proto and each handler's descriptor defaults, but two paths still left existing deployments broken: 1. deriveSchedulerAdminRuntime returned stored AdminRuntime configs as-is. Persisted configs from older versions have no execution_timeout_seconds, so the scheduler fell back to the 90s default — worse than the prior 240s behavior. Overlay descriptor defaults for any zero numeric fields when loading. 2. The admin form did not round-trip execution_timeout_seconds, so a normal save would clear it back to zero. Add the input field, the fillAdminSettings/collectAdminSettings hooks, and as defense in depth reapply descriptor defaults in UpdatePluginJobTypeConfigAPI before persisting so a stale form can never silently clobber a baseline. * fix(volume_balance): account for partial scheduling rounds in batch estimate With N moves and C slots, the busiest slot processes ceil(N/C) moves, not N/C. Dividing total seconds by C underestimates wall-clock time whenever N is not a multiple of C — e.g. 6 moves at concurrency 5 needs 2 rounds, not 1.2. Use avg * ceil(N/C) so partial rounds are counted as full ones. * fix(volume_balance): scale minBudget per wave instead of per move Orchestration overhead (setup/teardown for the parallel move runner) happens once per wave, not once per move. Use numRounds*60 as the floor instead of len(moves)*60 so the minimum doesn't inflate linearly with batch size when individual moves are tiny.